Packages
library(DT)
library(adabag)
FALSE Loading required package: rpart
FALSE Loading required package: caret
FALSE Loading required package: ggplot2
FALSE Loading required package: lattice
FALSE Loading required package: foreach
FALSE Loading required package: doParallel
FALSE Loading required package: iterators
FALSE Loading required package: parallel
library(rpart.plot)
library(pROC)
FALSE Type 'citation("pROC")' for a citation.
FALSE
FALSE Attaching package: 'pROC'
FALSE The following objects are masked from 'package:stats':
FALSE
FALSE cov, smooth, var
library(summarytools)
library(corrplot)
FALSE corrplot 0.92 loaded
library(dplyr)
FALSE
FALSE Attaching package: 'dplyr'
FALSE The following objects are masked from 'package:stats':
FALSE
FALSE filter, lag
FALSE The following objects are masked from 'package:base':
FALSE
FALSE intersect, setdiff, setequal, union
library(GGally)
FALSE Registered S3 method overwritten by 'GGally':
FALSE method from
FALSE +.gg ggplot2
library(fastDummies)
library(ggcorrplot)
library(klaR)
FALSE Loading required package: MASS
FALSE
FALSE Attaching package: 'MASS'
FALSE The following object is masked from 'package:dplyr':
FALSE
FALSE select
library(psych)
FALSE
FALSE Attaching package: 'psych'
FALSE The following objects are masked from 'package:ggplot2':
FALSE
FALSE %+%, alpha
library(MASS)
library(devtools)
FALSE Loading required package: usethis
library(ggplot2)
library(ggthemes)
library(GGally)
library(caret)
library(splitTools)
library(rpart)
library(xgboost)
FALSE
FALSE Attaching package: 'xgboost'
FALSE The following object is masked from 'package:dplyr':
FALSE
FALSE slice
library(caTools)
library(dplyr)
library(caret)
library(naniar)
CM_Function <- function(cm) {
layout(matrix(c(1,1,2)))
par(mar=c(2,2,2,2))
plot(c(100, 345), c(300, 450), type = "n", xlab="", ylab="", xaxt='n', yaxt='n')
title('CONFUSION MATRIX', cex.main=2)
# create the matrix
rect(150, 430, 240, 370, col='#2F4F4E')
text(195, 435, 'No', cex=1.2)
rect(250, 430, 340, 370, col='#0D8387')
text(295, 435, 'Yes', cex=1.2)
text(125, 370, 'Predicted', cex=1.3, srt=90, font=2)
text(245, 450, 'Actual', cex=1.3, font=2)
rect(150, 305, 240, 365, col='#0D8387')
rect(250, 305, 340, 365, col='#2F4F4E')
text(140, 400, 'No', cex=1.2, srt=90)
text(140, 335, 'Yes', cex=1.2, srt=90)
# add in the cm results
res <- as.numeric(cm$table)
text(195, 400, res[1], cex=1.6, font=2, col='white')
text(195, 335, res[2], cex=1.6, font=2, col='white')
text(295, 400, res[3], cex=1.6, font=2, col='white')
text(295, 335, res[4], cex=1.6, font=2, col='white')
# add in the specifics
plot(c(100, 0), c(100, 0), type = "n", xlab="", ylab="", main = "DETAILS", xaxt='n', yaxt='n')
text(10, 85, names(cm$byClass[1]), cex=1.2, font=2)
text(10, 70, round(as.numeric(cm$byClass[1]), 3), cex=1.2)
text(30, 85, names(cm$byClass[2]), cex=1.2, font=2)
text(30, 70, round(as.numeric(cm$byClass[2]), 3), cex=1.2)
text(50, 85, names(cm$byClass[5]), cex=1.2, font=2)
text(50, 70, round(as.numeric(cm$byClass[5]), 3), cex=1.2)
text(70, 85, names(cm$byClass[6]), cex=1.2, font=2)
text(70, 70, round(as.numeric(cm$byClass[6]), 3), cex=1.2)
text(90, 85, names(cm$byClass[7]), cex=1.2, font=2)
text(90, 70, round(as.numeric(cm$byClass[7]), 3), cex=1.2)
# add in the accuracy information
text(30, 35, names(cm$overall[1]), cex=1.5, font=2)
text(30, 20, round(as.numeric(cm$overall[1]), 3), cex=1.4)
text(70, 35, names(cm$overall[2]), cex=1.5, font=2)
text(70, 20, round(as.numeric(cm$overall[2]), 3), cex=1.4)
}
DATA AND QUICK FACTORING
df <- readxl::read_xls('Cchurn.xls')
df$international_plan <- factor(df$international_plan, levels = c('no', 'yes'), labels = c('0','1'))
df$voice_mail_plan <- factor(df$voice_mail_plan, levels = c('no', 'yes'), labels = c('0','1'))
df$churn <- factor(df$churn, levels = c('no', 'yes'), labels = c('0','1'))
SUMMARY
print(summarytools::dfSummary(df), method = 'render')
- We have no missing values -> perfect
- Heavily uneven counts of dependent variable (86 % no / 14 % yes)
-> maybe sample for equality / maybe not because we loose information
of other data
- Independent variables are on different scales -> standardize
- two (maybe three) categorical predictors: International plan /
voice_mail_plan (/ maybe number_customer_service_calls) -> dummy
encode -> not necessary as already 0 and 1
- Rest of data is numeric and most of the variables looks normally
distributed with exception of number_vmail_messages and totat_intl_calls
- transform these value to make them normal?
- maybe make parts of them categorical? (recieving voice mail or not,
calling internationally or not)
- or maybe the categorical values that we have already give an
indication for this
- Test normality of variables
- Can variables be combined? We have day / eve / night / intl calls
and for each of them minutes / calls / charge. Maybe we can combine this
into one metric. Maybe average cost per minute or average cost per
call?
CORRELATION PLOT BEFORE DATA ENGINEERING
df_numeric <- select_if(df, is.numeric) # Subset numeric columns with dplyr
M <- cor(df_numeric)
p.mat <- cor_pmat(df_numeric)
ggcorrplot(M, hc.order = TRUE, type = "lower", lab = TRUE, p.mat = p.mat, sig.level=0.05, lab_size = 2, tl.cex = 10,outline.col = "white", ggtheme = ggplot2::theme_minimal(), colors = c("#2F4F4E", "white", "#0D8387"))

Proves theory from before -> we can make one metric out of charge
and minutes –> charge / minutes
DATA ENGINEERING
df$total_day_charge_per_minute <- ifelse(df$total_day_minutes == 0, 0, df$total_day_charge / df$total_day_minutes)
df$total_eve_charge_per_minute <- ifelse(df$total_eve_minutes == 0, 0, df$total_eve_charge / df$total_eve_minutes)
df$total_night_charge_per_minute <- ifelse(df$total_night_minutes == 0, 0, df$total_night_charge / df$total_night_minutes)
df$total_intl_charge_per_minute <- ifelse(df$total_intl_minutes == 0, 0, df$total_intl_charge / df$total_intl_minutes)
df <- subset(df, select = -c(total_day_charge, total_day_minutes, total_eve_charge, total_eve_minutes, total_night_charge, total_night_minutes, total_intl_charge, total_intl_minutes))
CORRELATION PLOT AFTER DATA ENGINEERING
df_numeric <- select_if(df, is.numeric) # Subset numeric columns with dplyr
M <- cor(df_numeric)
p.mat <- cor_pmat(df_numeric)
ggcorrplot(M, hc.order = TRUE, type = "lower", lab = TRUE, p.mat = p.mat, sig.level=0.05, lab_size = 2, tl.cex = 10,outline.col = "white", ggtheme = ggplot2::theme_minimal(), colors = c("#2F4F4E", "white", "#0D8387"))

Now we have non-correlated data
HIGHER ORDER FEATURES
Only squaring as we have no negative data. Cubing would be needed
with negative data.
# squared
df2 <- df^2
df2 <- df2[,-c(2,3,10)]
colnames(df2) <- paste0(colnames(df2), '_sqd')
df <- cbind(df,df2)
Relationship between data in higher order
# theme_set(theme_minimal())
#
# ggpairs(
# data = df,
# columns = c(1:9, 11:25),
# mapping = aes(col = churn, alpha = .9)
# ) +
# scale_fill_colorblind() +
# scale_color_colorblind()
SAMPLING METHODS
As we have unbalanced data we need to use a sampling method to
balance the classes. Hereby there are four different methods. OVER /
UNDER / BOTH / ROSE.
library(ROSE)
FALSE Loaded ROSE 0.0-4
# OVER
df_OVER <- ovun.sample(churn~., data = df, method = "over")$data
table(df$churn)
FALSE
FALSE 0 1
FALSE 4293 707
table(df_OVER$churn)
FALSE
FALSE 0 1
FALSE 4293 4235
# UNDER
df_UNDER <- ovun.sample(churn~., data = df, method = "under")$data
table(df$churn)
FALSE
FALSE 0 1
FALSE 4293 707
table(df_UNDER$churn)
FALSE
FALSE 0 1
FALSE 672 707
# BOTH
df_BOTH <- ovun.sample(churn~., data = df, method = "both")$data
table(df$churn)
FALSE
FALSE 0 1
FALSE 4293 707
table(df_BOTH$churn)
FALSE
FALSE 0 1
FALSE 2524 2476
# ROSE
df_ROSE <- ROSE(churn ~ ., data = df, seed = 1, p = 0.5)$data
SAMPLING POST VISUALIZATION
# theme_set(theme_minimal())
#
# ggpairs(
# data = df_ROSE,
# columns = c(1:9, 11:25),
# mapping = aes(col = churn, alpha = .9)
# ) +
# scale_fill_colorblind() +
# scale_color_colorblind() +
# labs(title = "Machine Learning Project")
#
# ggpairs(
# data = df_OVER,
# columns = c(1:9, 11:25),
# mapping = aes(col = churn, alpha = .9)
# ) +
# scale_fill_colorblind() +
# scale_color_colorblind() +
# labs(title = "Machine Learning Project")
# ggpairs(
# data = df_UNDER,
# columns = c(1:9, 11:25),
# mapping = aes(col = churn, alpha = .9)
# ) +
# scale_fill_colorblind() +
# scale_color_colorblind() +
# labs(title = "Machine Learning Project")
# ggpairs(
# data = df_BOTH,
# columns = c(1:9, 11:25),
# mapping = aes(col = churn, alpha = .9)
# ) +
# scale_fill_colorblind() +
# scale_color_colorblind() +
# labs(title = "Machine Learning Project")
TRAIN AND TEST SPLIT
As we need to test the models we need to split the sampled data.
set.seed(1)
data <- df_OVER # choose which data to use df_ROSE / df_BOTH / df_UNDER / df_OVER / df
inds <- splitTools::partition(data$churn, p = c(train = 0.7, test = 0.3))
dftrain <- data[inds$train,]
dftest <- data[inds$test,]
SCALING
As some methods need scaled data we scale the data here to be
centered.
norm.value <- preProcess(dftrain, method = c("center", "scale"))
dftrain <- predict(norm.value, dftrain)
dftest <- predict(norm.value, dftest)
PREDICTIVE MODELS
NEURAL NET
# dftrain <- dftrain |>
# mutate_if(is.factor, as.character) |>
# mutate_if(is.character, as.numeric)
#
# library(neuralnet)
# mod.neural <- neuralnet(churn ~ ., data = dftrain, hidden=c(15,15), linear.output = FALSE)
#
# predicted.neural <- predict(mod.neural, dftest[,-c(10)])
#
# confmat.neural <- confusionMatrix(data=predicted.neural, reference = dftest$churn, positive = '1')
#
# CM_Function(confmat.neural)
#
# roc_score.neural =roc(factor(dftest$churn, ordered=TRUE), factor(predicted.neural, ordered=TRUE))
# plot(roc_score.neural ,main ="ROC curve")
BOOSTING
set.seed(123)
# train bagged model
mod.boost <- boosting(churn ~., data=dftrain)
predicted.boost <- factor(predict(mod.boost, dftest, type="class")$class)
confmat.boost <- confusionMatrix(data=predicted.boost, reference = dftest$churn, positive = '1')
CM_Function(confmat.boost)

roc_score.boost =roc(factor(dftest$churn, ordered=TRUE), factor(predicted.boost, ordered=TRUE))
FALSE Setting levels: control = 0, case = 1
FALSE Setting direction: controls < cases
plot(roc_score.boost ,main ="ROC curve")

CTREE
tree_full <- rpart(churn ~ .,
data = dftrain,
method = "class", # "class" because Y is a binary factor
minbucket = 1,
cp = 0.00001)
# Plot tree
rpart.plot(tree_full, yesno = TRUE, digits =-6)

min_xerr<- which.min(tree_full$cptable[,"xerror"]) # select minimum cross-validation error
cp_bp <- tree_full$cptable[min_xerr,"CP"] # find the corresponding CP value, to get the "best pruned " tree
mod.pruned_tree<- prune(tree_full, cp = cp_bp) # re-compute the tree with the selected Cp
rpart.plot(mod.pruned_tree, yesno = TRUE, digits =-3)

predicted.pruned_tree <- predict(mod.pruned_tree, dftest[,-c(10)], type = "class")
confmat.prunned_tree <- confusionMatrix(data=predicted.pruned_tree, reference = dftest$churn, positive = '1')
CM_Function(confmat.prunned_tree)

roc_score.prunned_tree =roc(factor(dftest$churn, ordered=TRUE), factor(predicted.pruned_tree, ordered=TRUE))
FALSE Setting levels: control = 0, case = 1
FALSE Setting direction: controls < cases
plot(roc_score.prunned_tree ,main ="ROC curve")

BAGGING
set.seed(123)
library(ipred)
FALSE
FALSE Attaching package: 'ipred'
FALSE The following object is masked from 'package:adabag':
FALSE
FALSE bagging
library(pROC)
# train bagged model
ames_bag1 <- bagging(
formula = churn ~ .,
data = dftrain,
nbagg = 100,
coob = TRUE,
control = rpart.control(minsplit = 2, cp = 0)
)
ames_bag1
FALSE
FALSE Bagging classification trees with 100 bootstrap replications
FALSE
FALSE Call: bagging.data.frame(formula = churn ~ ., data = dftrain, nbagg = 100,
FALSE coob = TRUE, control = rpart.control(minsplit = 2, cp = 0))
FALSE
FALSE Out-of-bag estimate of misclassification error: 0.0439
predicted <- factor(ifelse(predict(ames_bag1, dftest[,-c(10)], type = 'prob')[,2] >= 0.5, 1, 0))
CM_Function(confusionMatrix(data=predicted, reference = dftest$churn, positive = '1'))

roc_score=roc(factor(dftest$churn, ordered=TRUE), factor(predicted, ordered=TRUE)) #AUC score
FALSE Setting levels: control = 0, case = 1
FALSE Setting direction: controls < cases
auc <- round(auc(factor(dftest$churn, ordered=TRUE), factor(predicted, ordered=TRUE)),4)
FALSE Setting levels: control = 0, case = 1
FALSE Setting direction: controls < cases
ggroc(roc_score, colour = '#0D8387', size = 1) +
ggtitle(paste0('ROC Curve ', '(AUC = ', auc, ')')) + theme_minimal() + theme(plot.title = element_text(face = "bold")) + labs(x="Specificity", y="Sensitivity")

KNN
set.seed(1)
df <- data.frame(k = seq(1, 30, 1), accuracy = rep(0, 30), sensitivity = rep(0, 30))
# iterating over different ks
for(i in 1:30){
# nearest neighbor
KNN1 <- knn3(y = dftrain$churn, x = dftrain[,-c(10)], k = i)
# predictions response
KNN1.pred.valid.resp <- predict(KNN1, dftest[,-c(10)], type = "class")
# predictions prob
KNN1.pred.valid.prob <- predict(KNN1, dftest[,-c(10)], type = "prob")[,2]
# Confusionmatrix
df$sensitivity[i] <- confusionMatrix(KNN1.pred.valid.resp, dftest$churn, positive = "1")$byClass[1]
df$accuracy[i] <- confusionMatrix(KNN1.pred.valid.resp, dftest$churn, positive = "1")$overall[1]
}
# plot the k's
ggplot(df, aes(x=k)) +
geom_line(aes(y = sensitivity, colour = "Sensitivity")) +
geom_line(aes(y = accuracy, colour = "Accuracy")) +
labs(x = "Number of k nearest neighbours",
y = "Accuracy / Sensitivity", title = "Accuracy / Sensitivity regarding k") +
theme_minimal() +
scale_y_continuous(name = "Sensitivity / Accuracy", limits = c(0.7, 1)) +
scale_color_manual(name = "Values", values = c("Sensitivity" = "darkblue", "Accuracy" = "red")) +
xlim (1, 30)

mod.knn <- knn3(y = dftrain$churn, x = dftrain[,-c(10)], k = 2)
predicted.knn <- predict(mod.knn, dftest[,-c(10)], type = "class")
confmat.knn <- confusionMatrix(data=predicted.knn, reference = dftest$churn, positive = '1')
CM_Function(confmat.knn)

roc_score.qda =roc(factor(dftest$churn, ordered=TRUE), factor(predicted.knn, ordered=TRUE))
FALSE Setting levels: control = 0, case = 1
FALSE Setting direction: controls < cases
plot(roc_score.qda ,main ="ROC curve")

QDA
mod.qda <- qda(churn ~., data = dftrain)
predicted.qda <- predict(mod.qda, dftest[,-c(10)])$class
confmat.qda <- confusionMatrix(data=predicted.qda, reference = dftest$churn, positive = '1')
CM_Function(confmat.qda)

roc_score.qda =roc(factor(dftest$churn, ordered=TRUE), factor(predicted.qda, ordered=TRUE))
FALSE Setting levels: control = 0, case = 1
FALSE Setting direction: controls < cases
plot(roc_score.qda ,main ="ROC curve")

QLOG
mod.log <- glm(churn ~., data = dftrain, family = binomial(link = "probit"))
s <- step(mod.log)
FALSE Start: AIC=6830.44
FALSE churn ~ account_length + international_plan + voice_mail_plan +
FALSE number_vmail_messages + total_day_calls + total_eve_calls +
FALSE total_night_calls + total_intl_calls + number_customer_service_calls +
FALSE total_day_charge_per_minute + total_eve_charge_per_minute +
FALSE total_night_charge_per_minute + total_intl_charge_per_minute +
FALSE account_length_sqd + number_vmail_messages_sqd + total_day_calls_sqd +
FALSE total_eve_calls_sqd + total_night_calls_sqd + total_intl_calls_sqd +
FALSE number_customer_service_calls_sqd + total_day_charge_per_minute_sqd +
FALSE total_eve_charge_per_minute_sqd + total_night_charge_per_minute_sqd +
FALSE total_intl_charge_per_minute_sqd
FALSE
FALSE Df Deviance AIC
FALSE - account_length_sqd 1 6780.4 6828.4
FALSE - total_day_calls 1 6780.4 6828.4
FALSE - total_day_charge_per_minute 1 6780.5 6828.5
FALSE - total_day_charge_per_minute_sqd 1 6780.5 6828.5
FALSE - total_intl_charge_per_minute 1 6780.6 6828.6
FALSE - total_intl_charge_per_minute_sqd 1 6780.7 6828.7
FALSE - total_day_calls_sqd 1 6780.7 6828.7
FALSE - account_length 1 6781.1 6829.1
FALSE - total_night_calls 1 6781.1 6829.1
FALSE - total_night_calls_sqd 1 6781.3 6829.3
FALSE - total_eve_calls_sqd 1 6781.6 6829.6
FALSE - total_eve_calls 1 6781.7 6829.7
FALSE <none> 6780.4 6830.4
FALSE - total_night_charge_per_minute_sqd 1 6782.7 6830.7
FALSE - total_night_charge_per_minute 1 6782.9 6830.9
FALSE - number_vmail_messages_sqd 1 6787.5 6835.5
FALSE - number_vmail_messages 1 6790.8 6838.8
FALSE - total_intl_calls_sqd 1 6792.4 6840.4
FALSE - total_intl_calls 1 6798.9 6846.9
FALSE - number_customer_service_calls 1 6802.5 6850.5
FALSE - total_eve_charge_per_minute 1 6802.6 6850.6
FALSE - total_eve_charge_per_minute_sqd 1 6802.6 6850.6
FALSE - voice_mail_plan 1 6807.8 6855.8
FALSE - number_customer_service_calls_sqd 1 6939.3 6987.3
FALSE - international_plan 1 7403.1 7451.1
FALSE
FALSE Step: AIC=6828.44
FALSE churn ~ account_length + international_plan + voice_mail_plan +
FALSE number_vmail_messages + total_day_calls + total_eve_calls +
FALSE total_night_calls + total_intl_calls + number_customer_service_calls +
FALSE total_day_charge_per_minute + total_eve_charge_per_minute +
FALSE total_night_charge_per_minute + total_intl_charge_per_minute +
FALSE number_vmail_messages_sqd + total_day_calls_sqd + total_eve_calls_sqd +
FALSE total_night_calls_sqd + total_intl_calls_sqd + number_customer_service_calls_sqd +
FALSE total_day_charge_per_minute_sqd + total_eve_charge_per_minute_sqd +
FALSE total_night_charge_per_minute_sqd + total_intl_charge_per_minute_sqd
FALSE
FALSE Df Deviance AIC
FALSE - total_day_calls 1 6780.4 6826.4
FALSE - total_day_charge_per_minute 1 6780.5 6826.5
FALSE - total_day_charge_per_minute_sqd 1 6780.5 6826.5
FALSE - total_intl_charge_per_minute 1 6780.6 6826.6
FALSE - total_intl_charge_per_minute_sqd 1 6780.7 6826.7
FALSE - total_day_calls_sqd 1 6780.7 6826.7
FALSE - total_night_calls 1 6781.1 6827.1
FALSE - total_night_calls_sqd 1 6781.3 6827.3
FALSE - total_eve_calls_sqd 1 6781.6 6827.6
FALSE - total_eve_calls 1 6781.7 6827.7
FALSE <none> 6780.4 6828.4
FALSE - total_night_charge_per_minute_sqd 1 6782.7 6828.7
FALSE - total_night_charge_per_minute 1 6782.9 6828.9
FALSE - number_vmail_messages_sqd 1 6787.5 6833.5
FALSE - account_length 1 6790.1 6836.1
FALSE - number_vmail_messages 1 6790.8 6836.8
FALSE - total_intl_calls_sqd 1 6792.4 6838.4
FALSE - total_intl_calls 1 6798.9 6844.9
FALSE - number_customer_service_calls 1 6802.5 6848.5
FALSE - total_eve_charge_per_minute 1 6802.7 6848.7
FALSE - total_eve_charge_per_minute_sqd 1 6802.7 6848.7
FALSE - voice_mail_plan 1 6807.8 6853.8
FALSE - number_customer_service_calls_sqd 1 6939.5 6985.5
FALSE - international_plan 1 7403.4 7449.4
FALSE
FALSE Step: AIC=6826.44
FALSE churn ~ account_length + international_plan + voice_mail_plan +
FALSE number_vmail_messages + total_eve_calls + total_night_calls +
FALSE total_intl_calls + number_customer_service_calls + total_day_charge_per_minute +
FALSE total_eve_charge_per_minute + total_night_charge_per_minute +
FALSE total_intl_charge_per_minute + number_vmail_messages_sqd +
FALSE total_day_calls_sqd + total_eve_calls_sqd + total_night_calls_sqd +
FALSE total_intl_calls_sqd + number_customer_service_calls_sqd +
FALSE total_day_charge_per_minute_sqd + total_eve_charge_per_minute_sqd +
FALSE total_night_charge_per_minute_sqd + total_intl_charge_per_minute_sqd
FALSE
FALSE Df Deviance AIC
FALSE - total_day_charge_per_minute 1 6780.5 6824.5
FALSE - total_day_charge_per_minute_sqd 1 6780.5 6824.5
FALSE - total_intl_charge_per_minute 1 6780.6 6824.6
FALSE - total_intl_charge_per_minute_sqd 1 6780.7 6824.7
FALSE - total_night_calls 1 6781.1 6825.1
FALSE - total_night_calls_sqd 1 6781.3 6825.3
FALSE - total_eve_calls_sqd 1 6781.6 6825.6
FALSE - total_eve_calls 1 6781.7 6825.7
FALSE <none> 6780.4 6826.4
FALSE - total_night_charge_per_minute_sqd 1 6782.7 6826.7
FALSE - total_night_charge_per_minute 1 6782.9 6826.9
FALSE - number_vmail_messages_sqd 1 6787.5 6831.5
FALSE - account_length 1 6790.1 6834.1
FALSE - number_vmail_messages 1 6790.8 6834.8
FALSE - total_intl_calls_sqd 1 6792.4 6836.4
FALSE - total_day_calls_sqd 1 6794.1 6838.1
FALSE - total_intl_calls 1 6798.9 6842.9
FALSE - number_customer_service_calls 1 6802.6 6846.6
FALSE - total_eve_charge_per_minute 1 6802.7 6846.7
FALSE - total_eve_charge_per_minute_sqd 1 6802.7 6846.7
FALSE - voice_mail_plan 1 6807.9 6851.9
FALSE - number_customer_service_calls_sqd 1 6939.5 6983.5
FALSE - international_plan 1 7404.3 7448.3
FALSE
FALSE Step: AIC=6824.47
FALSE churn ~ account_length + international_plan + voice_mail_plan +
FALSE number_vmail_messages + total_eve_calls + total_night_calls +
FALSE total_intl_calls + number_customer_service_calls + total_eve_charge_per_minute +
FALSE total_night_charge_per_minute + total_intl_charge_per_minute +
FALSE number_vmail_messages_sqd + total_day_calls_sqd + total_eve_calls_sqd +
FALSE total_night_calls_sqd + total_intl_calls_sqd + number_customer_service_calls_sqd +
FALSE total_day_charge_per_minute_sqd + total_eve_charge_per_minute_sqd +
FALSE total_night_charge_per_minute_sqd + total_intl_charge_per_minute_sqd
FALSE
FALSE Df Deviance AIC
FALSE - total_intl_charge_per_minute 1 6780.7 6822.7
FALSE - total_intl_charge_per_minute_sqd 1 6780.8 6822.8
FALSE - total_night_calls 1 6781.1 6823.1
FALSE - total_night_calls_sqd 1 6781.3 6823.3
FALSE - total_eve_calls_sqd 1 6781.6 6823.6
FALSE - total_eve_calls 1 6781.7 6823.7
FALSE <none> 6780.5 6824.5
FALSE - total_night_charge_per_minute_sqd 1 6782.8 6824.8
FALSE - total_night_charge_per_minute 1 6782.9 6824.9
FALSE - number_vmail_messages_sqd 1 6787.6 6829.6
FALSE - account_length 1 6790.2 6832.2
FALSE - number_vmail_messages 1 6790.9 6832.9
FALSE - total_intl_calls_sqd 1 6792.4 6834.4
FALSE - total_day_calls_sqd 1 6794.1 6836.1
FALSE - total_day_charge_per_minute_sqd 1 6798.1 6840.1
FALSE - total_intl_calls 1 6799.0 6841.0
FALSE - number_customer_service_calls 1 6802.6 6844.6
FALSE - total_eve_charge_per_minute 1 6802.7 6844.7
FALSE - total_eve_charge_per_minute_sqd 1 6802.7 6844.7
FALSE - voice_mail_plan 1 6807.9 6849.9
FALSE - number_customer_service_calls_sqd 1 6939.6 6981.6
FALSE - international_plan 1 7404.3 7446.3
FALSE
FALSE Step: AIC=6822.67
FALSE churn ~ account_length + international_plan + voice_mail_plan +
FALSE number_vmail_messages + total_eve_calls + total_night_calls +
FALSE total_intl_calls + number_customer_service_calls + total_eve_charge_per_minute +
FALSE total_night_charge_per_minute + number_vmail_messages_sqd +
FALSE total_day_calls_sqd + total_eve_calls_sqd + total_night_calls_sqd +
FALSE total_intl_calls_sqd + number_customer_service_calls_sqd +
FALSE total_day_charge_per_minute_sqd + total_eve_charge_per_minute_sqd +
FALSE total_night_charge_per_minute_sqd + total_intl_charge_per_minute_sqd
FALSE
FALSE Df Deviance AIC
FALSE - total_night_calls 1 6781.3 6821.3
FALSE - total_night_calls_sqd 1 6781.5 6821.5
FALSE - total_eve_calls_sqd 1 6781.8 6821.8
FALSE - total_eve_calls 1 6781.9 6821.9
FALSE <none> 6780.7 6822.7
FALSE - total_night_charge_per_minute_sqd 1 6782.9 6822.9
FALSE - total_night_charge_per_minute 1 6783.0 6823.0
FALSE - number_vmail_messages_sqd 1 6787.7 6827.7
FALSE - account_length 1 6790.3 6830.3
FALSE - number_vmail_messages 1 6791.0 6831.0
FALSE - total_intl_calls_sqd 1 6792.6 6832.6
FALSE - total_intl_charge_per_minute_sqd 1 6794.1 6834.1
FALSE - total_day_calls_sqd 1 6794.3 6834.3
FALSE - total_day_charge_per_minute_sqd 1 6798.3 6838.3
FALSE - total_intl_calls 1 6799.1 6839.1
FALSE - number_customer_service_calls 1 6802.8 6842.8
FALSE - total_eve_charge_per_minute 1 6802.8 6842.8
FALSE - total_eve_charge_per_minute_sqd 1 6802.8 6842.8
FALSE - voice_mail_plan 1 6808.0 6848.0
FALSE - number_customer_service_calls_sqd 1 6939.6 6979.6
FALSE - international_plan 1 7404.3 7444.3
FALSE
FALSE Step: AIC=6821.29
FALSE churn ~ account_length + international_plan + voice_mail_plan +
FALSE number_vmail_messages + total_eve_calls + total_intl_calls +
FALSE number_customer_service_calls + total_eve_charge_per_minute +
FALSE total_night_charge_per_minute + number_vmail_messages_sqd +
FALSE total_day_calls_sqd + total_eve_calls_sqd + total_night_calls_sqd +
FALSE total_intl_calls_sqd + number_customer_service_calls_sqd +
FALSE total_day_charge_per_minute_sqd + total_eve_charge_per_minute_sqd +
FALSE total_night_charge_per_minute_sqd + total_intl_charge_per_minute_sqd
FALSE
FALSE Df Deviance AIC
FALSE - total_night_calls_sqd 1 6782.4 6820.4
FALSE - total_eve_calls_sqd 1 6782.4 6820.4
FALSE - total_eve_calls 1 6782.6 6820.6
FALSE <none> 6781.3 6821.3
FALSE - total_night_charge_per_minute_sqd 1 6783.6 6821.6
FALSE - total_night_charge_per_minute 1 6783.7 6821.7
FALSE - number_vmail_messages_sqd 1 6788.3 6826.3
FALSE - account_length 1 6790.8 6828.8
FALSE - number_vmail_messages 1 6791.6 6829.6
FALSE - total_intl_calls_sqd 1 6793.4 6831.4
FALSE - total_intl_charge_per_minute_sqd 1 6794.8 6832.8
FALSE - total_day_calls_sqd 1 6795.0 6833.0
FALSE - total_day_charge_per_minute_sqd 1 6799.3 6837.3
FALSE - total_intl_calls 1 6799.9 6837.9
FALSE - total_eve_charge_per_minute 1 6803.2 6841.2
FALSE - total_eve_charge_per_minute_sqd 1 6803.2 6841.2
FALSE - number_customer_service_calls 1 6803.4 6841.4
FALSE - voice_mail_plan 1 6808.6 6846.6
FALSE - number_customer_service_calls_sqd 1 6940.3 6978.3
FALSE - international_plan 1 7404.5 7442.5
FALSE
FALSE Step: AIC=6820.37
FALSE churn ~ account_length + international_plan + voice_mail_plan +
FALSE number_vmail_messages + total_eve_calls + total_intl_calls +
FALSE number_customer_service_calls + total_eve_charge_per_minute +
FALSE total_night_charge_per_minute + number_vmail_messages_sqd +
FALSE total_day_calls_sqd + total_eve_calls_sqd + total_intl_calls_sqd +
FALSE number_customer_service_calls_sqd + total_day_charge_per_minute_sqd +
FALSE total_eve_charge_per_minute_sqd + total_night_charge_per_minute_sqd +
FALSE total_intl_charge_per_minute_sqd
FALSE
FALSE Df Deviance AIC
FALSE - total_eve_calls_sqd 1 6783.5 6819.5
FALSE - total_eve_calls 1 6783.7 6819.7
FALSE <none> 6782.4 6820.4
FALSE - total_night_charge_per_minute_sqd 1 6784.6 6820.6
FALSE - total_night_charge_per_minute 1 6784.8 6820.8
FALSE - number_vmail_messages_sqd 1 6789.5 6825.5
FALSE - account_length 1 6791.9 6827.9
FALSE - number_vmail_messages 1 6792.7 6828.7
FALSE - total_intl_calls_sqd 1 6794.3 6830.3
FALSE - total_intl_charge_per_minute_sqd 1 6795.9 6831.9
FALSE - total_day_calls_sqd 1 6796.1 6832.1
FALSE - total_day_charge_per_minute_sqd 1 6800.6 6836.6
FALSE - total_intl_calls 1 6800.7 6836.7
FALSE - number_customer_service_calls 1 6804.1 6840.1
FALSE - total_eve_charge_per_minute 1 6804.1 6840.1
FALSE - total_eve_charge_per_minute_sqd 1 6804.1 6840.1
FALSE - voice_mail_plan 1 6809.8 6845.8
FALSE - number_customer_service_calls_sqd 1 6940.6 6976.6
FALSE - international_plan 1 7406.6 7442.6
FALSE
FALSE Step: AIC=6819.52
FALSE churn ~ account_length + international_plan + voice_mail_plan +
FALSE number_vmail_messages + total_eve_calls + total_intl_calls +
FALSE number_customer_service_calls + total_eve_charge_per_minute +
FALSE total_night_charge_per_minute + number_vmail_messages_sqd +
FALSE total_day_calls_sqd + total_intl_calls_sqd + number_customer_service_calls_sqd +
FALSE total_day_charge_per_minute_sqd + total_eve_charge_per_minute_sqd +
FALSE total_night_charge_per_minute_sqd + total_intl_charge_per_minute_sqd
FALSE
FALSE Df Deviance AIC
FALSE - total_eve_calls 1 6783.8 6817.8
FALSE <none> 6783.5 6819.5
FALSE - total_night_charge_per_minute_sqd 1 6785.7 6819.7
FALSE - total_night_charge_per_minute 1 6785.9 6819.9
FALSE - number_vmail_messages_sqd 1 6790.6 6824.6
FALSE - account_length 1 6793.1 6827.1
FALSE - number_vmail_messages 1 6793.9 6827.9
FALSE - total_intl_calls_sqd 1 6795.2 6829.2
FALSE - total_intl_charge_per_minute_sqd 1 6797.0 6831.0
FALSE - total_day_calls_sqd 1 6797.4 6831.4
FALSE - total_intl_calls 1 6801.5 6835.5
FALSE - total_day_charge_per_minute_sqd 1 6801.8 6835.8
FALSE - number_customer_service_calls 1 6805.1 6839.1
FALSE - total_eve_charge_per_minute 1 6805.2 6839.2
FALSE - total_eve_charge_per_minute_sqd 1 6805.2 6839.2
FALSE - voice_mail_plan 1 6810.9 6844.9
FALSE - number_customer_service_calls_sqd 1 6941.4 6975.4
FALSE - international_plan 1 7408.7 7442.7
FALSE
FALSE Step: AIC=6817.84
FALSE churn ~ account_length + international_plan + voice_mail_plan +
FALSE number_vmail_messages + total_intl_calls + number_customer_service_calls +
FALSE total_eve_charge_per_minute + total_night_charge_per_minute +
FALSE number_vmail_messages_sqd + total_day_calls_sqd + total_intl_calls_sqd +
FALSE number_customer_service_calls_sqd + total_day_charge_per_minute_sqd +
FALSE total_eve_charge_per_minute_sqd + total_night_charge_per_minute_sqd +
FALSE total_intl_charge_per_minute_sqd
FALSE
FALSE Df Deviance AIC
FALSE <none> 6783.8 6817.8
FALSE - total_night_charge_per_minute_sqd 1 6786.0 6818.0
FALSE - total_night_charge_per_minute 1 6786.1 6818.1
FALSE - number_vmail_messages_sqd 1 6790.9 6822.9
FALSE - account_length 1 6793.5 6825.5
FALSE - number_vmail_messages 1 6794.2 6826.2
FALSE - total_intl_calls_sqd 1 6795.4 6827.4
FALSE - total_intl_charge_per_minute_sqd 1 6797.3 6829.3
FALSE - total_day_calls_sqd 1 6797.7 6829.7
FALSE - total_intl_calls 1 6801.7 6833.7
FALSE - total_day_charge_per_minute_sqd 1 6802.1 6834.1
FALSE - number_customer_service_calls 1 6805.5 6837.5
FALSE - total_eve_charge_per_minute 1 6805.6 6837.6
FALSE - total_eve_charge_per_minute_sqd 1 6805.6 6837.6
FALSE - voice_mail_plan 1 6811.3 6843.3
FALSE - number_customer_service_calls_sqd 1 6941.7 6973.7
FALSE - international_plan 1 7408.7 7440.7
mod.log <- glm(s$formula, data = dftrain, family = binomial(link = "probit"))
predicted.log <- factor(ifelse(predict(mod.log, dftest[,-c(10)], type='response')>0.5,1,0))
confmat.log <- confusionMatrix(data=predicted.log, reference = dftest$churn, positive = '1')
CM_Function(confmat.log)

roc_score.log =roc(factor(dftest$churn, ordered=TRUE), factor(predicted.log, ordered=TRUE))
FALSE Setting levels: control = 0, case = 1
FALSE Setting direction: controls < cases
plot(roc_score.log ,main ="ROC curve")

GAUSSIAN SVM
library(e1071)
mod.svm = svm(formula = churn ~ .,
data = dftrain,
type = 'C-classification', # this is because we want to make a regression classification
kernel = 'radial')
predicted.svm <- predict(mod.svm, dftest[,-c(10)])
confmat.svm <- confusionMatrix(data=predicted.svm, reference = dftest$churn, positive = '1')
CM_Function(confmat.svm)

roc_score.svm =roc(factor(dftest$churn, ordered=TRUE), factor(predicted.svm, ordered=TRUE))
FALSE Setting levels: control = 0, case = 1
FALSE Setting direction: controls < cases
plot(roc_score.svm ,main ="ROC curve")

ENSEMBLES - MAJORITY VOTING
PRED_BOOSTING <- predicted.boost
PRED_PRUNED_TREE <- predicted.pruned_tree
PRED_BAGGING <- predicted
PRED_KNN <- predicted.knn
PRED_QDA <- predicted.qda
PRED_QLOG <- predicted.log
PRED_GAUSSIAN_SVM <- predicted.svm
ENSEMBLES <- cbind(PRED_BOOSTING,PRED_PRUNED_TREE,PRED_BAGGING,PRED_KNN,PRED_QDA)
ENSEMBLES <- as.data.frame(ENSEMBLES)
ENSEMBLES <- ifelse(ENSEMBLES == 2, 1, 0)
MAJORITY_VOTE <- rep(0,nrow(ENSEMBLES))
MAJORITY_VOTE <- ifelse(rowSums(ENSEMBLES) > (ncol(ENSEMBLES)-1)/2, 1, 0)
ENSEMBLES <- cbind(ENSEMBLES,MAJORITY_VOTE)
ENSEMBLES <- as.data.frame(ENSEMBLES)
ENSEMBLES$MAJORITY_VOTE <- as.factor(ENSEMBLES$MAJORITY_VOTE)
CM_ENSEMBLES <- confusionMatrix(data=ENSEMBLES$MAJORITY_VOTE, reference = dftest$churn, positive = '1')
CM_Function(CM_ENSEMBLES)

roc_score =roc(factor(dftest$churn, ordered=TRUE), factor(ENSEMBLES$MAJORITY_VOTE, ordered=TRUE))
FALSE Setting levels: control = 0, case = 1
FALSE Setting direction: controls < cases
auc <- round(auc(factor(dftest$churn, ordered=TRUE), factor(ENSEMBLES$MAJORITY_VOTE, ordered=TRUE)),4)
FALSE Setting levels: control = 0, case = 1
FALSE Setting direction: controls < cases
ggroc(roc_score, colour = '#0D8387', size = 1) +
ggtitle(paste0('ROC Curve ', '(AUC = ', auc, ')')) + theme_minimal() + theme(plot.title = element_text(face = "bold")) + labs(x="Specificity", y="Sensitivity")

PRESENTATION ASSETS
rm(list = ls())
# READ DATA
df <- readxl::read_xls('Cchurn.xls')
df$international_plan <- factor(df$international_plan, levels = c('no', 'yes'), labels = c('0','1'))
df$voice_mail_plan <- factor(df$voice_mail_plan, levels = c('no', 'yes'), labels = c('0','1'))
df$churn <- factor(df$churn, levels = c('no', 'yes'), labels = c('No','Yes'))
# DATA ENGINEERING
df$total_day_charge_per_minute <- ifelse(df$total_day_minutes == 0, 0, df$total_day_charge / df$total_day_minutes)
df$total_eve_charge_per_minute <- ifelse(df$total_eve_minutes == 0, 0, df$total_eve_charge / df$total_eve_minutes)
df$total_night_charge_per_minute <- ifelse(df$total_night_minutes == 0, 0, df$total_night_charge / df$total_night_minutes)
df$total_intl_charge_per_minute <- ifelse(df$total_intl_minutes == 0, 0, df$total_intl_charge / df$total_intl_minutes)
df <- subset(df, select = -c(total_day_charge, total_day_minutes, total_eve_charge, total_eve_minutes, total_night_charge, total_night_minutes, total_intl_charge, total_intl_minutes))
colnames(df) <- c("Account Length", "International Plan","Voice Mail Plan","Voice Mail Messages","Total Days Calls","Total Evening Calls","Total Night Calls","Total Internation Calls","Total Customer Service Call","Customer Churn","Total Day Charge/Minute", "Total Evening Charge/Minute","Total Night Charge/Minute","Total International Charge/Minute")
PAIRS PLOT
# Load the necessary libraries
library(ggplot2)
library(GGally)
library(ggthemes)
# Set the main color palette
colors <- c("#0D8387", "#870D27")
# Create the ggpair plot
PAIRS1 <- ggpairs(df,columns = c(1:4), mapping = aes(col = `Customer Churn`, alpha = 0.9)) + scale_color_manual(values = colors) + scale_fill_manual(values = colors) + labs(title = "Customer Telecommunication Data", subtitle = "Customer Churn = Yes is red", caption="From Variable 1 to 4") + theme(plot.title = element_text(face = "bold"))
PAIRS1
FALSE `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
FALSE `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
FALSE `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
FALSE `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.

PAIRS2 <- ggpairs(df,columns = c(5:9), mapping = aes(col = `Customer Churn`, alpha = 0.9)) + scale_color_manual(values = colors) + scale_fill_manual(values = colors) + labs(title = "Customer Telecommunication Data", subtitle = "Customer Churn = Yes is red", caption="From Variable 5 to 9") + theme(plot.title = element_text(face = "bold"))
PAIRS2

PAIRS3 <- ggpairs(df,columns = c(10:10), mapping = aes(col = `Customer Churn`, alpha = 0.9)) + scale_color_manual(values = colors) + scale_fill_manual(values = colors) + labs(title = "Customer Telecommunication Data", subtitle = "", y="Count") + theme(plot.title = element_text(face = "bold")) + theme_minimal() + theme(plot.title = element_text(face = "bold")) + annotate("text", x = 2, y = 900, label = "14.14%", colour = "#870D27", size=8) + annotate("text", x = 1, y = 4500, label = "85.86%", colour = "#0D8387", size=8) + theme(axis.text.x=element_text(size=16))
PAIRS3

PAIRS4 <- ggpairs(df,columns = c(11:14), mapping = aes(col = `Customer Churn`, alpha = 0.9)) + scale_color_manual(values = colors) + scale_fill_manual(values = colors) + labs(title = "Customer Telecommunication Data", subtitle = "Customer Churn = Yes is red",caption="From Variable 11 to 14") + theme(plot.title = element_text(face = "bold"))
PAIRS4

df$`Customer Churn` <- factor(df$`Customer Churn`, levels = c('No', 'Yes'), labels = c(0,1))
df$`Customer Churn` <- as.integer(df$`Customer Churn`)
df$`Customer Churn` <- df$`Customer Churn` -1
Proportions_Churn <- sum(df$`Customer Churn`[df$`Customer Churn` == 1])/nrow(df)
Proportions_No_Churn <- 1-Proportions_Churn
Proportions of Customer who churned => 14.14% Versus 85.86% who
didn’t churn.